This invention relates to processes and systems for dynamically and automatically measuring traffic across a communication network in order to ensure efficient allocation of network resources.
Network traffic management aims to cost-effectively minimize the number of unsuccessful communication attempts caused by network congestion or failure, while also ensuring that expensive network equipment is not over-or-under-used. The ultimate goal is to provide a given grade of service with the least amount of equipment. To do this, one must determine the amount of traffic handled by the network, particularly by switches in the network. Traffic data describes the amount and features of the communications (voice, video or data) traffic-through the network. It is collected to help operators of communication networks determine how efficiently those networks are operating and, if necessary, to plan network reductions, repairs or upgrades.
Traffic data also is helpful to large customers who rent or lease network facilities. The owner of this invention also owns a patent that describes a xe2x80x9cTelephone System Adapted to Report to Customers Telephone Facility Traffic Data.xe2x80x9d That U.S. Pat. No. 5,410,589, which describes the advantages of and procedures for collecting and reporting traffic data, is hereby incorporated in its entirety by this reference.
Two typical ways to engineer switch capacity include extreme value engineering, which engineers switches to accommodate the maximum traffic, or time consistent busy hour engineering, which engineers switches to accommodate the peak traffic during a period that, on average, is most busy. A tension exists, however, between providing quality versus cost-efficient service. Extreme value engineering provides maximum quality at high cost; time consistent busy hour engineering provides a chosen level of quality at a lower cost.
For example, a particular switch component may be servicing 256 lines but be provided with only 64 time slots by which those lines are serviced. (Line units allow the analog subscriber lines to communicate with the digital network). The time slots are less than the number of lines because more than 64 lines are rarely, if ever, in use at once. If, however, 65 lines seek access to communications services from the switch at the same time, the 65th caller is blocked by the switch. Typically, networks are engineered to keep blocking below a certain absolute percentage, such as 7% of attempted calls, and within a certain average percentage, such as 1.5% of attempted calls. By way of example, determining that congestion generated xe2x80x9cfast busyxe2x80x9d signals for over 2% of communications attempted through a particular switch at selected time periods tells network engineers (a) to expect customer complaints about poor service and (b) that the network may need a repair or an upgrade if the problem persists.
Typically, the enormous volume and types of traffic across network switches allowed collection and analysis of only statistically significant samples of traffic data, rather than collecting and analyzing traffic in real time. Thus, prior methods of analyzing switch traffic use the xe2x80x9caverage peak usage hourxe2x80x9d for the entire switch. Peak usage or busy hour refers to the hour of the day in which traffic across the switch hits a peak. (Although the phrase uses the term xe2x80x9chour,xe2x80x9d the peak usage can be determined over any time period). The xe2x80x9caveragexe2x80x9d refers to the fact that the average peak usage hour is selected by averaging the traffic across many days and then determining, the hour of the day during which peak usage of the network switch occurs. Generally, the average peak usage hour has been determined once per year by manually analyzing the switch traffic usage. The selected xe2x80x9caverage peak usage hourxe2x80x9d is then used for network traffic engineering for the remainder of the year, with switches engineered to handle the volume and type of traffic that occurs during their average peak usage hour.
For example, commercial systems like the COER system marketed by Lucent Technologies, Inc. (formerly part of ATandT) take traffic data from a particular switch and then organize and report that data to allow engineers manually to determine whether switch limits have been reached. This required that the traffic analysis process involve only a small subset of the available data. Also, the COER system determined busy hour only once a year and only for the whole switch.
Other efforts at traffic monitoring have been made. For instance, U.S. Pat. No. 4,456,788 to Kline, et al. describes a xe2x80x9cTelecommunication Trunk Circuit Reporter and Advisorxe2x80x9d system and method that analyses trunk circuit data. The Kline, et al. patent mentions determining busy hours, but does not describe doing so on a switch component or continuous basis. U.S. Pat. No. 5,359,649 describes systems for xe2x80x9cCongestion Tuning of Telecommunications Networksxe2x80x9d that monitor network elements and routes to identify congested routes and repair them or reroute traffic.
These prior processes and systems do not address several problems, however. First, a certain number of switch components are engineered beyond (or below) their capacity. That is because traffic data has been collected only for the average peak usage hour of the entire switch, which means that traffic data for this hour is the only data analyzed in engineering the network. But many switch components will have a different average peak usage hour; the same components may also have levels of traffic significantly different during their actual peak usage hour. This results in overburdened or under used switch components that may fail or be more expensive to operate.
Also, the average peak usage hour normally is determined only once a year. This was fine, in the past, when the relative stability of traffic usage across switches required determination of the average peak usage hour only yearly in order to support traffic engineering. The network equipment and customer assignment procedures used when prior traffic usage processes and systems were developed resulted in relatively homogeneous traffic usage across switch components, which also allowed infrequent selection of an average peak usage hour.
Statistical analysis of traffic data predicts the amount of blocking expected for a given level of switch usage. Two key measurements impact the amount of blocking: the traffic volume handled by the switch and the volatility of the traffic. Volume is typically measured in centume call seconds or xe2x80x9cCCSxe2x80x9d handled by a switch during an average busy hour determined according to the methods described above. Those methods effectively lower average usage capacity of all switch components to match the worst-performing component of the switch. Volatility refers to the degree of traffic variance from a calculated average. Volatility typically was dealt with by simply discarding traffic data collected for days that were believed to be unrepresentative. For example, in many systems, traffic data for holidays, Saturdays, Sundays and even Fridays was disgarded or ignored when analyzing switch loads. As volatility and capacity increase so does blocking.
Recently, however, numerous changes in technology and the industry have occurred. Those changes have drastically and negatively impacted the effectiveness of current processes in analyzing switching capacities.
For instance, two separate causes have resulted in non-homogenous assignment on switch components. First, subscriber carrier delivery systems have reduced the random spreading of the customer assignment process. Most subscriber carrier systems typically handle about 96 lines each and serve a very small geographic area. Because of this, systems often serve primarily only residential customers or only business customers. For example, new subdivisions-may have all of the residences therein assigned to a new switch component. Thus, a switch component handling customers located in a high-end residential area with many second lines and computer modems may handle significantly more and different traffic than components of the rest of the switch. Customers with different usage characteristics are concentrated on different switch components, leading to wildly different busy hours among the components.
A second reason for increasingly non-homogenous assignment is the practice of reusing previously assigned facilities when service to a new customer at an old location was introduced (e.g., when an old customer moves, the connection to the residence remains and service is simply restored when someone else reoccupies the residence). That significantly reduces labor associated with establishing new service at previously occupied locations, but results in less homogeneous traffic because new customers end up being served in a specific, new switch component.
Finally, there has been a constant and increasing proliferation of high use lines for internet service providers, telecommuting host computer connections, and the like. These new, numerous and high use lines also have increased the traffic load differences between various switch components.
Overall, these changes have reduced the homogeneous nature of the traffic load across switch components. Growing numbers of switch components accordingly have average peak usage hours that are different from the average peak usage hour for the whole switch and other components. Also, not only are the average peak usage hours different among components, but the xe2x80x9cpeakxe2x80x9d traffic handled by a particular component may. be significantly different from the xe2x80x9cpeakxe2x80x9d traffic handled by other components or the entire switch. This is especially exacerbated by grouping of new subscribers, who may use newer technologies, onto a single switch component, as described above. Furthermore, many of the weekend and holiday traffic data that was disgarded as unrepresentative actually is or is becoming more representative of overall switch traffic.
Numerous components are accordingly not being engineered for proper traffic load. Further, rapid growth in telecommunications has resulted in dynamically changing average peak usage hours. For example, as businesses increasingly provide internet access for their employees, switch traffic during the previously little used lunch hour has greatly increased as employees make use of a xe2x80x9cfreexe2x80x9d internet connection during lunch breaks. Better methods of filtering or eliminating non-representative traffic measurements are also needed.
In short, there is a need for traffic analysis systems and processes that dynamically respond to changes in average peak usage hour and that take account of actual loads across switch components when analyzing traffic usage. Ultimately, improved busy-hour determination and filtering result in more accurate determination of actual traffic usage on an individual switch component basis, which present processes and systems do not allow. That information is then used to (a) adjust the load on a particular switch or its components or (b) reengineer the network to a more optimal configuration for its actual load.
The present invention aims to accurately and dynamically determine the traffic capacity of individual switch components in order to provide improved service and to more effectively utilize network switching resources. The invention includes an automated process and system that dynamically and automatically determines the correct peak hour and average usage at that hour (or other time period) for selected components of network elements like a switch. The method involves the steps of periodically collecting and storing segments of traffic data on each switch component over a selected time period; averaging the traffic data of each switch component for each segment across the selected time period; and selecting the peak usage segment. Methods also are disclosed for filtering aberrant or statistically corrupting traffic data from the collected traffic data. Such methods dynamically determine whether particular traffic data is statistically unsuitable. The methods for determining correct average usage and peak hour may be repeated continuously in order to take account of changes in the network load and factor those changes into selection of the peak segment for a particular component. This, in turn, allows the method of the present invention to determine accurately and dynamically the capacity limit for each switch component.
Traffic data describing traffic across the switch component at the selected peak usage segment is collected and analyzed. Depending on the results, the load on the selected component may be adjusted or the network otherwise reconfigured. A system is disclosed that performs these processes automatically.
The dynamic and automatic process and system of the present invention quickly recognizes volatile traffic usage changes that take place in the communications network in order to increase the accuracy of traffic analysis. Using this process should increase service results, increase capital dollar savings, and reduce manpower requirements. For instance, more accurate usage data informs traffic engineers to take proactive measures to prevent new conditions from impacting service. Equipment can be deployed in more correct quantities in order to save capitalization costs. Replacing a manual process with an automatic one will reduce the manpowerxe2x80x94and related operational costsxe2x80x94required to perform the analysis.
In one embodiment of the invention, selected switch components generate and buffer traffic data. A collector periodically retrieves that traffic data, which describes use of the component over a selected time segment or period, such as 30 minutes. The collector provides the traffic data to a database and processor. Because the collector and processor can monitor numerous (e.g., hundreds on switches with multiple components, the database is helpful for maintaining the large volume of traffic data needed for selecting each component""s average peak segment.
The database contains at least enough traffic data to create a journal describing a representative- period of usage of the component. Generally, the journal should be long enough to minimize the impact of a grouping of higher-than-normal daily average peak usage segments, while short enough to respond to a fundamental change in usage of a particular switch or component. The journal acts like a moving window that analyzes the most recent, adjustable number of daily average peak segments to select the average peak usage segment for the journalling period. If a new day""s data is added to the journal, any day earlier than the previous 30 days (or other time frame that comprises the journalling period) can be ignored. Selecting an average peak usage segment from a rolling journalling period prevents a spike in a particular component""s busy hour from affecting selection of the average peak usage hour. In any event, collecting a journal full of traffic data ensures sufficient data to allow determination of an average peak usage segment that appropriately reflects average peak traffic across various switches and switch components.
The processor averages collected, filtered traffic data on each switch component for each segment across an entire journalling period. This process repeats until successive segments in a single day have been averaged with the corresponding 29 (for a 30 day journal) other segments. This determines the average usage of the component during each time segment. The result is an entire day comprising multiple segments with each showing the average usage. The segment at which peak traffic occurred on the component may then be selected. One selection method involves selecting from the traffic data the peak average use of two consecutive segments; this gives the switch component""s busy hour (or other time period) if the segments are 30 minute time periods.
Traffic data measurements for the selected peak hour are stored for each day in the database. The traffic data can be automatically filtered to eliminate aberrant data, i.e., data that embodies errors or that should be excluded from traffic sage engineering analysis because it falls outside of statistically acceptable bounds. Then, the processor averages the non-flagged traffic data in order to determine the average usage of a particular component at its busy hour.
This selection of a peak segment and determination of average usage during the peak segment may be performed daily upon the segments recorded in the journal, which is updated routinely so as to keep only the segments describing traffic over the most recent selected number of days. Because the process selects the average peak usage segment over a rolling, updated journal, changes in traffic patterns for particular switch components or switches during that time period will be dynamically and automatically detected. Generally, by the time the shift in peak usage has repeated for one-half of the journalling period, the data indicating a new average peak usage segment overcomes data supporting the old average peak usage segment. For example, if particular switch components have been handling traffic from a prison that allows inmates to make calls during the 5:00 p.m. to 6:00 p.m. period, that period likely will be the average peak segment. However, if prison officials permanently change the calling time to 7:00 p.m. to 8:00 p.m., the average peak segment would shift to this later period. Assuming a journalling period of 30 days, the present process would detect the shift within about 15 days. Accordingly, a temporary change of less than 15 days would not change the average peak segment.
Using data collected as described above, methods of the present invention may be used to more accurately determine capacity of individual switch components. First, non-representative traffic measurements for a particular component""s average peak usage segment are identified and removed from the data from which capacity is determined. Next, the method uses the remaining traffic data to determine an average traffic usage limit for the switch component.
In order to accurately determine which measurements truly are non-representative, the method of the invention selects a first mean value for all traffic data measurements at a selected average peak usage segment for a particular journalling period. Thus, if a component has a 2:30-3:30 p.m. average peak usage segment, the mean of the traffic at this time is determined for the prior 30 day journalling period. Next, a lower bound is established. The present invention selects the lower bound as a percentage of the first mean value. Preferably, 95% of the first mean value is used as the lower bound. This value has been found to exclude traffic measurements from low usage days not representative of normal, predominant traffic patterns. Relevant measurements remain to allow proper engineering of the component.
Next, an upper bound is established. This is done by determining a second mean value based upon traffic data for the journal (e.g., prior 30 days) that remains after traffic below the lower bound (e.g., 95% of the first mean) is excluded. The method then determines the standard deviation between the second mean and traffic data remaining above the lower bound. An upper bound can then be established based on the second mean plus 2 standard deviations. All daily peak usage segment measurements exceeding the upper bound are excluded.
By marking non-representative measurements in accordance with this method, the present invention more accurately eliminates actual non-representative traffic data, by contrast with prior methods, which simply made gross assumptions about which data was likely to be non-representative. Moreover, the method of the present invention detects changes in traffic patterns that create non-representative data. For example, while prior methods would not detect a snow day or the like during which traffic was particularly high, the present method detects and excludes such unrepresentative data.
With the data bounded by the lower and upper bounds, a third mean value that represents the average monthly traffic usage for the switch component may be determined. This third mean value is used as the average measurement of actual usage for engineering purposes. This value is compared against calculated usage capacity to determine proximity to service limits. It is also used in calculations of average usage per assigned line on the component, which can be used to estimate the number of individual subscribers that can be supported by the component. The usage measurements remaining after the above exclusion process can be used to compute a measurement of volatility exhibited by the data. This volatility measures is computed by the standard deviation of the usage measurement divided by the mean. The volatility measurement can be used to then compute the average usage capacity of the component.
A database stores the average usage for a busy hour as well as the traffic usage limit calculated for the switch components. Such traffic data may include busy hour usage, call attempts or call blocking. The average usage traffic data may then be compared to the component""s threshold capacities in order to manage the load on the component. Components that are at, near or over capacity can have their loads appropriately adjusted. An administration network may be provided that can access the stored traffic data and generate reports in order to determine whether objective service levels are being met by the communications network. For instance, various network managers may connect to a network information warehouse holding the collected traffic data for the selected average peak segments of various switch components. Network managers may then run reports to obtain service level measurements, like determinations of a particular component""s (or switch""s): percent dial tone delay; percent capacity; percent occupancy; percent blocking or any other measurements desired by network engineering.
The process of the present invention may be implemented on a computer platform in communication with a particular or numerous switches. For instance, in one embodiment, a personal computer may be coupled to selected switch components. In another embodiment, a warehouse computer platform may be coupled to a collector that polls multiple components of multiple switches in order to collect traffic data describing traffic handled by those switch components. For instance, the warehouse platform may couple to the collector that monitors traffic across switches located within a regional communications network, such as a Local Access and Transport Area (xe2x80x9cLATAxe2x80x9d), or multiple switches in regional networks. The warehouse platform stores collected traffic data and processes it as described above in order to select the average peak usage segment for a particular switch component.
The present invention accordingly aims to achieve at least one, more or combinations of the following objectives:
To accurately calculate the operating capacity of individual switch components in order to assign the correct number of lines to particular components.
To automatically select an average peak usage segment useful for allocating resources within a communications network and thereby make more efficient use of network resources and decrease network operating costs;
To collect and analyze traffic data from switch components in order to select the average peak usage segment of at least selected switch components;
To dynamically determine shifts in a particular component""s average peak usage segment;
To monitor traffic across selected switch components during that component""s selected average peak-usage segment and compare traffic at that time to the component""s capacity in order to adjust loads across selected switch components;
To support traffic engineering by analyzing traffic data collected during switch components"" average peak usage segments in order to generate various reports on switch components"" service levels;
To provide methods for eliminating non-representative traffic measurements for switch component engineering;
To more accurately determine limiting average traffic for switch components; and
To provide a system for implementing the methods of the present invention that achieve the above objectives.
Other objects, features and advantages of this invention will become apparent from the rest of this document, including the Figures.